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Record W1546973662 · doi:10.5539/ass.v11n17p48

Impacts on the Implementation of Social Policy: Comparative Study in Malaysia and Indonesia

2015· article· en· W1546973662 on OpenAlexvenueno aff
Mohmad Zahir Zainudin, Mohd Fauzi Kamarudin

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyEmpowermentPsychological interventionMicro creditWelfareEconomic growthInvestment (military)BusinessPublic economicsEconomicsPolitical sciencePsychologyPolitics

Abstract

fetched live from OpenAlex

Prolonged debate exists concerning the effectiveness of anti-poverty programs through micro-credit to thewell-being of the poor. Some studies showed that microcredit have positive impact to the poor people's lives.However, other studies stated that what actually seemed as a remedy is actually just an increase in the businessincome that does not necessarily bring about to a better well-being of the poor. This paper identifies the impactof the implementation of social interventions brought in through micro-credit schemes of poverty alleviationprograms for the welfare being of society in Malaysia and Indonesia. The methodology used was a combinationof the quantitative and qualitative methods aimed to get the maximum results based on Standard Model of SocialDevelopment. Data analysis performed on the respondents indicated four factors leading to the failure of thewelfare of the community. These factors are social networks, community participation, community developmentand employment opportunities. This research suggests that agencies involved in poverty alleviation throughmicro-credit schemes must carry out appropriate efforts towards the empowerment and active participation of therespondents in social and economic investment as stated in the Standard Model of Social Development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.349
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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